OpenRaiser/Intern-Atlas huggingface dataset:
A Tutorial on Six-Dimensional Movable Antenna for 6G Networks: Synergizing Positionable and Rotatable Antennas
Venue: arXiv cs.IT 2025
Year: 2025

Six-dimensional movable antenna (6DMA) is a new and revolutionary technique that fully exploits the wireless channel spatial variations at the transmitter/receiver by flexibly adjusting the three-dimensional (3D) positions and/or 3D rotations of antennas/antenna surfaces (sub-arrays), thereby improving the performance of wireless networks cost-effectively without the need to deploy additional antennas. It is thus expected that the integration of new 6DMAs into future sixth-generation (6G) wireless networks will fundamentally enhance antenna agility and adaptability, and introduce new degrees of freedom (DoFs) for system design. Despite its great potential, 6DMA faces new challenges to be efficiently implemented in wireless networks, including corresponding architectures, antenna position and rotation optimization, channel estimation, and system design from both communication and sensing perspectives. In this paper, we provide a tutorial on 6DMA-enhanced wireless networks to address the above issues by unveiling associated new channel models, hardware implementations and practical position/rotation constraints, as well as various appealing applications in wireless networks. Moreover, we discuss two special cases of 6DMA, namely, rotatable 6DMA with fixed antenna position and positionable 6DMA with fixed antenna rotation, and highlight their respective design challenges and applications. We further present prototypes developed for 6DMA-enhanced communication along with experimental results obtained with these prototypes. Finally, we outline promising directions for further investigation.
ShED-HD: A Shannon Entropy Distribution Framework for Lightweight Hallucination Detection on Edge Devices
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have demonstrated impressive capabilities on a broad array of NLP tasks, but their tendency to produce hallucinations$\unicode{x2013}$plausible-sounding but factually incorrect content$\unicode{x2013}$poses severe challenges in high-stakes domains. Existing hallucination detection methods either bear the computational cost of multiple inference passes or sacrifice accuracy for efficiency with single-pass approaches, neither of which is ideal in resource-constrained environments such as edge devices. We propose the Shannon Entropy Distribution Hallucination Detector (ShED-HD), a novel hallucination detection framework that bridges this gap by classifying sequence-level entropy patterns using a lightweight BiLSTM architecture with single-headed attention. In contrast to prior approaches, ShED-HD efficiently detects distinctive uncertainty patterns across entire output sequences, preserving contextual awareness. Through in-depth evaluation on three datasets (BioASQ, TriviaQA, and Jeopardy Questions), we show that ShED-HD significantly outperforms other computationally efficient approaches in the out-of-distribution setting, while achieving comparable performance in the in-distribution setting. ShED-HD facilitates hallucination detection that is low-cost, accurate, and generalizable, improving the credibility of content generated by LLMs in resource-constrained environments where trustworthy AI functionality is crucial.
Analyzing Islamophobic Discourse Using Semi-Coded Terms and LLMs
Venue: arXiv cs.LG 2025
Year: 2025

In recent years, Islamophobia has gained significant traction across Western societies, fueled by the rise of digital communication networks. This paper performs a large-scale analysis of specialized, semi-coded Islamophobic terms such as (muzrat, pislam, mudslime, mohammedan, muzzies) floated on extremist social platforms, i.e., 4Chan, Gab, Telegram, etc. Many of these terms appear lexically neutral or ambiguous outside of specific contexts, making them difficult for both human moderators and automated systems to reliably identify as hate speech. First, we use Large Language Models (LLMs) to show their ability to understand these terms. Second, Google Perspective API suggests that Islamophobic posts tend to receive higher toxicity scores than other categories of hate speech like Antisemitism. Finally, we use BERT topic modeling approach to extract different topics and Islamophobic discourse on these social platforms. Our findings indicate that LLMs understand these Out-Of-Vocabulary (OOV) slurs; however, further improvements in moderation strategies and algorithmic detection are necessary to address such discourse effectively. Our topic modeling also indicates that Islamophobic text is found across various political, conspiratorial, and far-right movements and is particularly directed against Muslim immigrants. Taken altogether, we performed one of the first studies on Islamophobic semi-coded terms and shed a global light on Islamophobia.
Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture
Venue: arXiv cs.IT 2025
Year: 2025

Over-the-air computation (AirComp) has emerged as an essential approach for enabling communication-efficient federated learning (FL) over wireless networks. Nonetheless, the inherent analog transmission mechanism in AirComp-based FL (AirFL) intensifies challenges posed by potential Byzantine attacks. In this paper, we propose a novel Byzantine-robust FL paradigm for over-the-air transmissions, referred to as federated learning with secure adaptive clustering (FedSAC). FedSAC aims to protect a portion of the devices from attacks through zero trust architecture (ZTA) based Byzantine identification and adaptive device clustering. By conducting a one-step convergence analysis, we theoretically characterize the convergence behavior with different device clustering mechanisms and uneven aggregation weighting factors for each device. Building upon our analytical results, we formulate a joint optimization problem for the clustering and weighting factors in each communication round. To facilitate the targeted optimization, we propose a dynamic Byzantine identification method using historical reputation based on ZTA. Furthermore, we introduce a sequential clustering method, transforming the joint optimization into a weighting optimization problem without sacrificing the optimality. To optimize the weighting, we capitalize on the penalty convex-concave procedure (P-CCP) to obtain a stationary solution. Numerical results substantiate the superiority of the proposed FedSAC over existing methods in terms of both test accuracy and convergence rate.
Surgical Action Planning with Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

In robot-assisted minimally invasive surgery, we introduce the Surgical Action Planning (SAP) task, which generates future action plans from visual inputs to address the absence of intraoperative predictive planning in current intelligent applications. SAP shows great potential for enhancing intraoperative guidance and automating procedures. However, it faces challenges such as understanding instrument-action relationships and tracking surgical progress. Large Language Models (LLMs) show promise in understanding surgical video content but remain underexplored for predictive decision-making in SAP, as they focus mainly on retrospective analysis. Challenges like data privacy, computational demands, and modality-specific constraints further highlight significant research gaps. To tackle these challenges, we introduce LLM-SAP, a Large Language Models-based Surgical Action Planning framework that predicts future actions and generates text responses by interpreting natural language prompts of surgical goals. The text responses potentially support surgical education, intraoperative decision-making, procedure documentation, and skill analysis. LLM-SAP integrates two novel modules: the Near-History Focus Memory Module (NHF-MM) for modeling historical states and the prompts factory for action planning. We evaluate LLM-SAP on our constructed CholecT50-SAP dataset using models like Qwen2.5 and Qwen2-VL, demonstrating its effectiveness in next-action prediction. Pre-trained LLMs are tested in a zero-shot setting, and supervised fine-tuning (SFT) with LoRA is implemented. Our experiments show that Qwen2.5-72B-SFT surpasses Qwen2.5-72B with a 19.3% higher accuracy.
RAU: Towards Regularized Alignment and Uniformity for Representation Learning in Recommendation
Venue: arXiv cs.IR 2025
Year: 2025

Recommender systems (RecSys) have become essential in modern society, driving user engagement and satisfaction across diverse online platforms. Most RecSys focuses on designing a powerful encoder to embed users and items into high-dimensional vector representation space, with loss functions optimizing their representation distributions. Recent studies reveal that directly optimizing key properties of the representation distribution, such as alignment and uniformity, can outperform complex encoder designs. However, existing methods for optimizing critical attributes overlook the impact of dataset sparsity on the model: limited user-item interactions lead to sparse alignment, while excessive interactions result in uneven uniformity, both of which degrade performance. In this paper, we identify the sparse alignment and uneven uniformity issues, and further propose Regularized Alignment and Uniformity (RAU) to cope with these two issues accordingly. RAU consists of two novel regularization methods for alignment and uniformity to learn better user/item representation. 1) Center-strengthened alignment further aligns the average in-batch user/item representation to provide an enhanced alignment signal and further minimize the disparity between user and item representation. 2) Low-variance-guided uniformity minimizes the variance of pairwise distances along with uniformity, which provides extra guidance to a more stabilized uniformity increase during training. We conducted extensive experiments on three real-world datasets, and the proposed RAU resulted in significant performance improvements compared to current state-of-the-art CF methods, which confirms the advantages of the two proposed regularization methods.
Ground Penetrating Radar-Assisted Multimodal Robot Odometry Using Subsurface Feature Matrix
Venue: arXiv cs.RO 2025
Year: 2025

Localization of robots using subsurface features observed by ground-penetrating radar (GPR) enhances and adds robustness to common sensor modalities, as subsurface features are less affected by weather, seasons, and surface changes. We introduce an innovative multimodal odometry approach using inputs from GPR, an inertial measurement unit (IMU), and a wheel encoder. To efficiently address GPR signal noise, we introduce an advanced feature representation called the subsurface feature matrix (SFM). The SFM leverages frequency domain data and identifies peaks within radar scans. Additionally, we propose a novel feature matching method that estimates GPR displacement by aligning SFMs. The integrations from these three input sources are consolidated using a factor graph approach to achieve multimodal robot odometry. Our method has been developed and evaluated with the CMU-GPR public dataset, demonstrating improvements in accuracy and robustness with real-time performance in robotic odometry tasks.
DiffMove: Group Mobility Tendency Enhanced Trajectory Recovery via Diffusion Model
Venue: arXiv cs.AI 2025
Year: 2025

In the real world, trajectory data is often sparse and incomplete due to low collection frequencies or limited device coverage. Trajectory recovery aims to recover these missing trajectory points, making the trajectories denser and more complete. However, this task faces two key challenges: 1) The excessive sparsity of individual trajectories makes it difficult to effectively leverage historical information for recovery; 2) Sparse trajectories make it harder to capture complex individual mobility preferences. To address these challenges, we propose a novel method called DiffMove. Firstly, we harness crowd wisdom for trajectory recovery. Specifically, we construct a group tendency graph using the collective trajectories of all users and then integrate the group mobility trends into the location representations via graph embedding. This solves the challenge of sparse trajectories being unable to rely on individual historical trajectories for recovery. Secondly, we capture individual mobility preferences from both historical and current perspectives. Finally, we integrate group mobility tendencies and individual preferences into the spatiotemporal distribution of the trajectory to recover high-quality trajectories. Extensive experiments on two real-world datasets demonstrate that DiffMove outperforms existing state-of-the-art methods. Further analysis validates the robustness of our method.
Bridging Writing Manner Gap in Visual Instruction Tuning by Creating LLM-aligned Instructions
Venue: arXiv cs.AI 2025
Year: 2025

In the realm of Large Multi-modal Models (LMMs), the instruction quality during the visual instruction tuning stage significantly influences the performance of modality alignment. In this paper, we assess the instruction quality from a unique perspective termed \textbf{Writing Manner}, which encompasses the selection of vocabulary, grammar and sentence structure to convey specific semantics. We argue that there exists a substantial writing manner gap between the visual instructions and the base Large Language Models (LLMs) within LMMs. This gap forces the pre-trained base LLMs to deviate from their original writing styles, leading to capability degradation of both base LLMs and LMMs. To bridge the writing manner gap while preserving the original semantics, we propose directly leveraging the base LLM to align the writing manner of soft-format visual instructions with that of the base LLM itself, resulting in novel LLM-aligned instructions. The manual writing manner evaluation results demonstrate that our approach successfully minimizes the writing manner gap. By utilizing LLM-aligned instructions, the baseline models LLaVA-7B and QwenVL demonstrate enhanced resistance to hallucinations and non-trivial comprehensive improvements across all $15$ visual and language benchmarks.
LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives
Venue: arXiv cs.CV 2025
Year: 2025

Light detection and ranging (LiDAR) remote sensing encompasses two major directions: data interpretation and parameter inversion. However, both directions rely heavily on costly and labor-intensive labeled data and field measurements, which constrains their scalability and spatiotemporal adaptability. Weakly Supervised Learning (WSL) provides a unified framework to address these limitations. This paper departs from the traditional view that treats interpretation and inversion as separate tasks and offers a systematic review of recent advances in LiDAR remote sensing from a unified WSL perspective. We cover typical WSL settings including incomplete supervision(e.g., sparse point labels), inexact supervision (e.g., scene-level tags), inaccurate supervision (e.g., noisy labels), and cross-domain supervision (e.g., domain adaptation/generalization) and corresponding techniques such as pseudo-labeling, consistency regularization, self-training, and label refinement, which collectively enable robust learning from limited and weak annotations.We further analyze LiDAR-specific challenges (e.g., irregular geometry, data sparsity, domain heterogeneity) that require tailored weak supervision, and examine how sparse LiDAR observations can guide joint learning with other remote-sensing data for continuous surface-parameter retrieval. Finally, we highlight future directions where WSL acts as a bridge between LiDAR and foundation models to leverage large-scale multimodal datasets and reduce labeling costs, while also enabling broader WSL-driven advances in generalization, open-world adaptation, and scalable LiDAR remote sensing.
ED-DAO: Energy Donation Algorithms based on Decentralized Autonomous Organization
Venue: arXiv cs.DC 2025
Year: 2025

Energy is a fundamental component of modern life, driving nearly all aspects of daily activities. As such, the inability to access energy when needed is a significant issue that requires innovative solutions. In this paper, we propose ED-DAO, a novel fully transparent and community-driven decentralized autonomous organization (DAO) designed to facilitate energy donations. We analyze the energy donation process by exploring various approaches and categorizing them based on both the source of donated energy and funding origins. We propose a novel Hybrid Energy Donation (HED) algorithm, which enables contributions from both external and internal donors. External donations are payments sourced from entities such as charities and organizations, where energy is sourced from the utility grid and prosumers. Internal donations, on the other hand, come from peer contributors with surplus energy. HED prioritizes donations in the following sequence: peer-sourced energy (P2D), utilitygrid-sourced energy (UG2D), and direct energy donations by peers (P2PD). By merging these donation approaches, the HED algorithm increases the volume of donated energy, providing a more effective means to address energy poverty. Experiments were conducted on a dataset to evaluate the effectiveness of the proposed method. The results showed that HED increased the total donated energy by at least 0.43% (64 megawatts) compared to the other algorithms (UG2D, P2D, and P2PD).
Robust-IR @ SIGIR 2025: The First Workshop on Robust Information Retrieval
Venue: arXiv cs.IR 2025
Year: 2025

With the advancement of information retrieval (IR) technologies, robustness is increasingly attracting attention. When deploying technology into practice, we consider not only its average performance under normal conditions but, more importantly, its ability to maintain functionality across a variety of exceptional situations. In recent years, the research on IR robustness covers theory, evaluation, methodology, and application, and all of them show a growing trend. The purpose of this workshop is to systematize the latest results of each research aspect, to foster comprehensive communication within this niche domain while also bridging robust IR research with the broader community, and to promote further future development of robust IR. To avoid the one-sided talk of mini-conferences, this workshop adopts a highly interactive format, including round-table and panel discussion sessions, to encourage active participation and meaningful exchange among attendees.
Large Language Models powered Malicious Traffic Detection: Architecture, Opportunities and Case Study
Venue: arXiv cs.NI 2025
Year: 2025

Malicious traffic detection is a pivotal technology for network security to identify abnormal network traffic and detect network attacks. Large Language Models (LLMs) are trained on a vast corpus of text, have amassed remarkable capabilities of context-understanding and commonsense knowledge. This has opened up a new door for network attacks detection. Researchers have already initiated discussions regarding the application of LLMs on specific cyber-security tasks. Unfortunately, there remains a lack of comprehensive analysis on harnessing LLMs for traffic detection, as well as the opportunities and challenges. In this paper, we focus on unleashing the full potential of Large Language Models (LLMs) in malicious traffic detection. We present a holistic view of the architecture of LLM-powered malicious traffic detection, including the procedures of Pre-training, Fine-tuning, and Detection. Especially, by exploring the knowledge and capabilities of LLM, we identify three distinct roles LLM can act in traffic classification: Classifier, Encoder, and Predictor. For each of them, the modeling paradigm, opportunities and challenges are elaborated. Finally, we present our design on LLM-powered DDoS detection as a case study. The proposed framework attains accurate detection on carpet bombing DDoS by exploiting LLMs' capabilities in contextual mining. The evaluation shows its efficacy, exhibiting a nearly 35% improvement compared to existing systems.
RoboTron-Nav: A Unified Framework for Embodied Navigation Integrating Perception, Planning, and Prediction
Venue: arXiv cs.RO 2025
Year: 2025

In language-guided visual navigation, agents locate target objects in unseen environments using natural language instructions. For reliable navigation in unfamiliar scenes, agents should possess strong perception, planning, and prediction capabilities. Additionally, when agents revisit previously explored areas during long-term navigation, they may retain irrelevant and redundant historical perceptions, leading to suboptimal results. In this work, we propose RoboTron-Nav, a unified framework that integrates perception, planning, and prediction capabilities through multitask collaborations on navigation and embodied question answering tasks, thereby enhancing navigation performances. Furthermore, RoboTron-Nav employs an adaptive 3D-aware history sampling strategy to effectively and efficiently utilize historical observations. By leveraging large language model, RoboTron-Nav comprehends diverse commands and complex visual scenes, resulting in appropriate navigation actions. RoboTron-Nav achieves an 81.1% success rate in object goal navigation on the $\mathrm{CHORES}$-$\mathbb{S}$ benchmark, setting a new state-of-the-art performance. Project page: https://yvfengzhong.github.io/RoboTron-Nav
HiRes-FusedMIM: A High-Resolution RGB-DSM Pre-trained Model for Building-Level Remote Sensing Applications
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in self-supervised learning have led to the development of foundation models that have significantly advanced performance in various computer vision tasks. However, despite their potential, these models often overlook the crucial role of high-resolution digital surface models (DSMs) in understanding urban environments, particularly for building-level analysis, which is essential for applications like digital twins. To address this gap, we introduce HiRes-FusedMIM, a novel pre-trained model specifically designed to leverage the rich information contained within high-resolution RGB and DSM data. HiRes-FusedMIM utilizes a dual-encoder simple masked image modeling (SimMIM) architecture with a multi-objective loss function that combines reconstruction and contrastive objectives, enabling it to learn powerful, joint representations from both modalities. We conducted a comprehensive evaluation of HiRes-FusedMIM on a diverse set of downstream tasks, including classification, semantic segmentation, and instance segmentation. Our results demonstrate that: 1) HiRes-FusedMIM outperforms previous state-of-the-art geospatial methods on several building-related datasets, including WHU Aerial and LoveDA, demonstrating its effectiveness in capturing and leveraging fine-grained building information; 2) Incorporating DSMs during pre-training consistently improves performance compared to using RGB data alone, highlighting the value of elevation information for building-level analysis; 3) The dual-encoder architecture of HiRes-FusedMIM, with separate encoders for RGB and DSM data, significantly outperforms a single-encoder model on the Vaihingen segmentation task, indicating the benefits of learning specialized representations for each modality. To facilitate further research and applications in this direction, we will publicly release the trained model weights.